335 citations · 389 across the 5 of their papers we have counts for
5 papers
Privacy-preserving medical image analysis
Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel +8
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy pr…
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Cheng Hong, Zhicong Huang, Wen-jie Lu +4
Machine learning (ML) methods have been widely used in genomic studies. However, genomic data are often held by different stakeholders (e.g. hospitals, universities, and healthcare…
Detecting Spiky Corruption in Markov Decision Processes
Jason Mancuso, Tomasz Kisielewski, David Lindner +1
Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem w…
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valu…
Private Machine Learning in TensorFlow using Secure Computation
Morten Dahl, Jason Mancuso, Yann Dupis +5
We present a framework for experimenting with secure multi-party computation directly in TensorFlow. By doing so we benefit from several properties valuable to both researchers and…